A robust decision-support framework for urban green space planning: Integrating biophysical supply and social vulnerability for thermal resilience in a high-density city

Source abstract

Under the combined pressures of global warming and rapid urbanisation, the Urban Heat Island (UHI) effect continues to intensify, yet the spatial relationship between the supply and demand of Thermal Regulation Ecosystem Service (TR-ES) remains insufficiently understood. Using Fuzhou, a typical high-density subtropical city, as a case study, this study develops a census unit scale TR-ES supply–demand–Priority Index (PRI) assessment framework with robustness constraints. The framework combines the InVEST urban cooling model with an Effective Demand Index (H d ) constructed under the IPCC exposure–sensitivity–adaptive capacity framework, and evaluates multiple PRI formulations through sensitivity and uncertainty analyses. This allows priority intervention streets to be identified while explicitly accounting for methodological uncertainty. The results reveal a clear structural mismatch in Fuzhou, with high TR-ES supply and low demand in peripheral areas, but low supply and high demand in the urban core. Land surface temperature, population density, and NDVI show strong spatial correspondence with the mismatch pattern. Higher vegetation cover is generally associated with lower mismatch levels. In contrast, central built-up areas are characterised by the co-occurrence of high heat exposure, dense population concentration, and limited ecological buffering. Based on the robust PRI results, 27 priority intervention streets were identified, and the main intervention pathways were grouped into four action types: Exposure Reduction, Sensitivity Reduction, Supply Enhancement, and Adaptive Capacity Improvement. Overall, this study provides a transferable analytical framework and spatial decision support for precise heat governance and climate adaptation planning in high-density cities.